Few-shot image classification aims to learn knowledge with a few labeled samples quickly.
Recent few-shot image classification methods can be divided into two types.
The first type of method [1]}, [2]}, [3]} is called inductive inference, where each unlabeled instance is predicted independently.
Although these methods perform well, they ignore the information of unlabeled query instances.
Thus, another type of method called transductive inference [4]}, [5]} is proposed to explore the information of the unlabeled instances.
Since transduction inference methods need to use pseudo-labels of unlabeled instances in the query set during the training phase, they often use few-shot learning models to obtain pseudo-labels and adopt classification scores as confidence.
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